Prior attempts towards models unifying  assessment and learning sciences have had limited success~\cite{mohyunjose2014,fast2014}
because often the item difficulty is underestimated and confounds with learning estimates. 
We take the complementary strategy of using assessment methods to improve skill definitions.
ASCEND is similar to  Learning Factors Analysis (LFA)~\cite{cen_factor_analysis}, an automatic skill  refinement  algorithm.
However LFA requires the labor-intensive task of having experts to annotate \yh{check} potential new difficulty factors for every item of the pool.
Modern tutoring systems may have thousands of items, and LFA may be very costly to implement in practice.


We propose \methodname, an automatic method to improve the existing item to skill mapping. \methodname improves 33\% of the expert ill-defined skills and the algorithm (framework) is easy to be implemented. The main contributions of our work are: \yh{Check=>} 
(1) we are the first to consider item difficulty discrepencies within a skill in that has traditionally been ignored in skill refinement frameworks, and to achieve this, we bring the insight from assessment method IRT that has been well developed and understood, 
(2)  \methodname is able to detect latent new skill components (difficulty factors) automatically without complex modeling of item-to-skill or skill-to-skill relationship, 
and (3) we also push forward the the learning curve analysis brining in item difficulty. 

Our method \methodname takes the simplification that the ill definition of a skill is the reason of a non-decreasing learning curve. However, we are aware that some other reasons could exist (e.g. students not having enough practices), and we would further investigate in the future. Also, we are working on utilizing calibrated homework data IRT estimation in order to retrieve more item difficulty estimates since currently we have limited overlapping items with test data. The improvement using test data is promising and our preliminary results suggest using non-calibrated homework item difficulty estimates can also improve the skill definition but with less magnitude.

